The Acceptance and Use of Mobile Learning for Kuwaiti Government Employee Training: Views from the Perspectives of UTAUT
Bibliographic record
Abstract
With the rapid advancements of technology, Kuwait is one of the countries taking on the efforts to implement mobile learning strategies for employee training. By leveraging the Unified Theory of Technology Acceptance and Use (UTAUT), this study centers on the acceptance and use of mobile learning for Kuwaiti Government employee training. A qualitative approach was used in a case study on Kuwait's public sector personnel, focusing on the Kuwait Civil Service Commission’s (KCSC) eTraining initiative since 2008, involving interviews with ten employees. The study reveals that workplace mobile learning acceptance and use are influenced by employees' digital skills, mobile learning infrastructure, resources, and workplace standards and policy. Employees obtained specialized information and abilities necessary for their job through deliberate learning activities outlined in the mobile learning standards and rules. The implementation of a comprehensive mobile learning infrastructure and the utilization of advanced hardware have significantly enhanced the efficacy of technology in the workplace. The results of the study highlighted the significance of digital skills in the workplace in relation to mobile learning aspects and job demands. This study has provided significant insights for the public sector in Kuwait, considering the scarcity of existing research on this subject matter.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".